i am trying to create a cnn_learner for my test set via from_csv. My plan is to set valid_pct=1.0 so that I can easily plot my toplosses using ClassificationInterpretation.from_learner(learn_train).
In the docs there is nothing about a limit for setting the % for the valid_set. But i am getting an error
Exception: Can't infer the type of your targets. It's either because your data source is empty or because your labelling function raised an error.
Can someone explain to me what this is about?
@classmethod def from_csv(cls, path:PathOrStr, folder:PathOrStr=None, label_delim:str=None, csv_labels:PathOrStr='labels.csv', valid_pct:float=0.2, seed:int=None, fn_col:int=0, label_col:int=1, suffix:str='', delimiter:str=None, header:Optional[Union[int,str]]='infer', **kwargs:Any)->'ImageDataBunch': "Create from a csv file in `path/csv_labels`." path = Path(path) df = pd.read_csv(path/csv_labels, header=header, delimiter=delimiter) return cls.from_df(path, df, folder=folder, label_delim=label_delim, valid_pct=valid_pct, seed=seed, fn_col=fn_col, label_col=label_col, suffix=suffix, **kwargs)